Topic Analysis to Enhance Automated Resolution Rates
Topics is a gated feature that requires approval from the platform team for activation. Contact yellow.ai's support team for more details.
In customer service automation, understanding and leveraging conversation data is pivotal. One of the most powerful tools at your disposal is the analysis of topics automatically generated by your AI-agent. This analysis helps identify opportunities to improve your automated resolution rate, leading to a more efficient and satisfying customer experience.
When your AI-agent generates topics, it categorizes conversations based on the themes discussed. These topics are crucial for identifying areas where your AI-agent can improve its performance. The default sorting of topics by Contained Resolution (CR) opportunity highlights the most significant opportunities for enhancing your AI-agent's automated resolution rate.
To access topics page:
- Log in to the yellow.ai platform.
- Open Analyse > Conversation logs.
- Navigate to Topics.
Key metrics for topic analysis
Article suggestion
Based on the conversations AI has processed, articles are suggested for each topic with Article suggestion icon highlighted. These suggestions can serve as new additions to the existing knowledge base or as training materials.
Learn more here.
Automation Opportunity
Automation Opportunity opportunity metric represents the total opportunity a topic has to improve your overall automated resolution rate. It is calculated as:
Automation Opportunity = Unresolved conversations under a topic/Unresolved conversations across all topics
This helps identify which topics have the most potential for improvement.
Conversation share
This metric shows the proportion of conversations involving a particular topic compared to all conversations:
Conversation Share = Conversations under a topic/Conversations across all topics
It helps prioritize topics based on their frequency.
For example, in the below screenshot, Out of 809 conversations taken place in this AI-agent, 29 belong to this topic.
AI resolution rate
The AI resolution rate indicates the percentage of conversations on a topic that were successfully resolved by the AI-agent without human intervention:
AI resolution rate = Conversations in this topic that were contained AND resolved/Conversations in this topic
A higher AI resolution rate signifies better AI-agent performance in resolving issues autonomously.
Automation rate
This metric measures the percentage of conversations on a topic that were not escalated to a human agent:
Automation Rate = Conversations in this topic not handed over to a human agent/Conversations in this topic
A higher Automation rate indicates greater efficiency in handling the topic without needing human support.
User sentiment
This metric assesses the sentiment of users during conversations about a specific topic. It shows the percentage of positive, negative and neutral conversations that have taken place while discussing about this topic. Understanding user sentiment helps in identifying areas where the AI-agent's responses might need improvement to enhance customer satisfaction.
Search & Filter Topics
You can search for a specific topic using the global search feature.
To filter data and view a particular topic, click Filter.
The following filters are available:
| Feature | Description | UI |
|---|---|---|
| Article Suggested | Select either "Yes" or "No." |  |
| Timestamp | Filter data for specific dates or a custom time range. |  |
| Topics | Filter by the text or subtext of the topics. |  |
Utilize topics for AI-agent improvement
By closely monitoring these metrics, you can gain actionable insights into your AI-agent's performance and identify areas for enhancement. Here are some steps to leverage topic analysis effectively:
- Prioritize high-opportunity topics: Focus on topics with high CR Opportunity to make the most significant impact on your automated resolution rate. These are the areas where improving the AI-agent's responses can yield the highest returns.
- Analyze low containment rate topics: Investigate topics with low containment rates to understand why users are being escalated to human agents. This can help in refining the AI-agent's responses or providing better training data.
- Enhance CR Rate: For topics with lower CR Rates, consider revising the AI-agent’s dialogue scripts, adding more detailed FAQs, or improving the AI-agent’s understanding through advanced natural language processing (NLP) techniques.
- Monitor sentiment: Keep an eye on user sentiment for each topic. If users consistently express negative sentiments, it’s a signal that the AI-agent’s handling of that topic needs improvement.
- Iterate and test: Regularly update and test the AI-agent’s responses based on the insights gained from topic analysis. Continuous iteration helps in gradually enhancing the AI-agent’s performance and increasing the automated resolution rate.